Neuroengineering

“… by maneuvering his right shoulder in certain ways, Mumford could send signals through the stimulator and down his left arm into the muscles of his hand … He [Previously paralyzed] could open the refrigerator, take out a sandwich, and eat it on his own.

Each concept that we learn, we build an internal model of that concept. There is a “summarized model” of each concept we learn – that comes to our mind just as we think of that concept.

So, for example, thinking of “Microsoft” could remind you of Bill Gates or the Windows Operating System you have on your laptop. But Microsoft is not just Bill Gates or not just Windows. Gates or Windows are only the “summarized model or representation” of Microsoft in your brain.The problem with this is that it could make us fall into the trap of “stereotyping” the world and not reflect the totality of a concept but only a part of it.

As an instance, it might happen that you have read the famous novel “Godfather” by Mario Puzo and from that point on, whenever you hear of Italy or Italians, you are reminded of Italian Mafia. But that’s stereotyping. Not all Italians are part of a Mafia gang.How do we build these models?We build these models as we learn concepts, possibly in a social context.This applies to every domain.

Let me give you an example from Marketing.A few days back I wrote:

Microsoft has lost it’s “Brand Appeal” in the past few years that it once enjoyed. Google and Apple lead Microsoft in terms of “Brand Appeal”.

When you think of Google or Apple products you think of them as being “cool”, “awesome”, “wonderful”, and so on.How?That’s how you learned about Google or Apple. You heard your friends say, “Apple products are so cool” and that’s how the model of “Apple products” in your mind got represented, as being something “cool”. In Marketing jargon, it’s called “word of mouth” – advertising through the mouth of satisfied customers.“Brand Appeal” depends more on what people “think” of products than the products themselves.It might be the case that Microsoft products are better, but people are not doing enough of those “Wow”s –

“Windows is so cool!”

or

“Surface is simply sensational!”

In other words, “Brand Appeal”could fall victim to human stereotyping.The effect is not just on customers and consumers, but also on job seekers – when you look for jobs, you certainly want to work for the “coolest” company around.

The Fields Medal is given every four years, and several can be awarded at once. The other recipients this year are Artur Avila of the National Institute of Pure and Applied Mathematics in Brazil and the National Center for Scientific Research in France; Manjul Bhargava of Princeton University; and Martin Hairer of the University of Warwick in England.

Much of the research by Dr. Mirzakhani, who was born in Tehran in 1977, has involved the behavior of dynamical systems. There are no exact mathematical solutions for many dynamical systems, even simple ones.

“What Maryam discovered is that in another regime, the dynamical orbits are tightly constrained to follow algebraic laws,” said Curtis T. McMullen, a professor at Harvard who was Dr. Mirzakhani’s doctoral adviser. “These dynamical systems describe surfaces with many handles, like pretzels, whose shape is evolving over time by twisting and stretching in a precise way. They are related to billiards on tables that are not rectangular but still polygonal, like the regular octagon.”

Congratulations to American – Iranian Mathematician Maryam Mirzakhani! When I used to look at the ranklist of International Mathematical Olympiad (IMO), I found that the only Muslim-majority country that made it to the top was Iran. Maryam Mirzakhani was one of the young mathematicians who represented Iran in International Mathematical Olympiad and “At the 1995 International Mathematical Olympiad she was the first Iranian student to finish with a perfect score.” [1]

Fields Medal winner Maryam Mirzakhani’s work reminds me of an area (complex systems) that I am currently thinking a lot about. Just as you can’t solve dynamical systems exactly, but can discover algebraic laws that constrain a particular variety of dynamical system, in complex systems research, you can’t model and exactly predict a complex system consisting of lots and lots of interacting agents, but there are emergent properties (and regularities) that you discover when you view from a different perspective. So maybe in near future we will discover an algebra for describing emergent properties in complex systems, like cells (consisting of interacting molecules) or brains (consisting of interacting neurons), or ecology (consisting of interacting organisms).

Trying to understand the brain by creating exact models of the brain with enormous computing power (costing Billions of dollars) is not going to work – as is intended.

We are never going to understand how the brain works completely with data only from the lowest level (molecules, channels, neurons) with so much complexity involved.

There is so much genetic variation from mouse to mouse, primate to primate, that you can’t draw general conclusions from data of genetic expression of a single mouse. What happens in the brain data scanning initiatives is that data is gathered from a single organism. And what is required is something similar to functional genomics – sort of functional neuroscience – trying to understand the relation between behavior and what happens in the brain – not just cataloging what data from a particular brain looks like.

What we need is new models, new theories, new abstractions – that can explain all these data.

We don’t need to simulate large parts of the brain on computers. Our goal should be simulation of small parts and theoretical models that can explain data from those small parts of the brain.

We need to start building models, theories, abstractions. And then on top our first attempts at building models and theories, we will start building more accurate models, models that connect data from different levels of the brain.

Our first attempts at building models, abstractions might concentrate on data from only one level. Next, our newer models would connect different levels of structural abstractions and their corresponding different levels of functional abstractions found in the brain.

Different structural levels of abstractions found in the brain:

Molecules, Receptors, Neurotransmitters.

Neuron, Channels, Synapse, Glial Cells.

Collection of neurons

Brain regions (e.g., Primary Visual Cortex)

Brain – Behavior;

Neuroscientists individually work on a tiny part at “only one level” (among all these levels, from molecules and neurons to whole brain) of the brain. We need scientists who can connect different levels of structural abstractions.

The new breed of Neuroscientists, with the aim of building models, abstractions, theories of the brain, would try to learn how scientists with different backgrounds are studying Neuroscience.

What diseases are Neurologists seeing in patients? How do the Neurologists explain them in terms of lesions, etc. in a particular brain region?

Examples:

Speech – Broca’s area [2].

Synesthesia [3] – Cross-connections among nearby brain regions.

What diseases are Psychiatrists seeing in patients? How do they explain them in terms of excess or reduction in neurotransmitters?

Examples:

Schizophrenia – Excess of Dopamine [4].

Data from neurons, channels, molecules.

Data from specific brain regions (e.g., MRI, fMRI data).

Data from optogenetics – switching neurons on and off with light.

Systems Neuroscience

Computer Models of brain. Connectomics.

What are we learning from our research in Artificial Intelligence about the requirements of intelligence?

Psychologists have built models. Researchers interested in both Artificial Intelligence and Neuroscience (e.g., Marvin Minsky [1]) have built models. Why not start by trying to explain those models with our understanding of the brain?